Structural Insights into Plant Viruses Revealed by Small-Angle X-ray Scattering and Atomic Force Microscopy.

The structural study of plant viruses is of great importance to reduce the damage caused by these agricultural pathogens and to support their biotechnological applications. Nowadays, X-ray crystallography, NMR spectroscopy and cryo-electron microscopy are well accepted methods to obtain the 3D protein structure with the best resolution. However, for large and complex supramolecular structures such as plant viruses, especially flexible filamentous ones, there are a number of technical limitations to resolving their native structure in solution. In addition, they do not allow us to obtain structural information about dynamics and interactions with physiological partners. For these purposes, small-angle X-ray scattering (SAXS) and atomic force microscopy (AFM) are well established. In this review, we have outlined the main principles of these two methods and demonstrated their advantages for structural studies of plant viruses of different shapes with relatively high spatial resolution. In addition, we have demonstrated the ability of AFM to obtain information on the mechanical properties of the virus particles that are inaccessible to other experimental techniques. We believe that these under-appreciated approaches, especially when used in combination, are valuable tools for studying a wide variety of helical plant viruses, many of which cannot be resolved by classical structural methods.

A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database.

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

CKD Urine Metabolomics: Modern Concepts and Approaches.

One of the primary challenges regarding chronic kidney disease (CKD) diagnosis is the absence of reliable methods to detect early-stage kidney damage. A metabolomic approach is expected to broaden the current diagnostic modalities by enabling timely detection and making the prognosis more accurate. Analysis performed on urine has several advantages, such as the ease of collection using noninvasive methods and its lower protein and lipid content compared with other bodily fluids. This review highlights current trends in applied analytical methods, major discoveries concerning pathways, and investigated populations in the context of urine metabolomic research for CKD over the past five years. Also, we are presenting approaches, instrument upgrades, and sample preparation modifications that have improved the analytical parameters of methods. The onset of CKD leads to alterations in metabolism that are apparent in the molecular composition of urine. Recent works highlight the prevalence of alterations in the metabolic pathways related to the tricarboxylic acid cycle and amino acids. Including diverse patient cohorts, using numerous analytical techniques with modifications and the appropriate annotation and explanation of the discovered biomarkers will help develop effective diagnostic models for different subtypes of renal injury with clinical applications.

Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification.

Plant samples are potential sources of physiologically active secondary metabolites and their classification is an extremely important task in traditional medicine and other fields of research. In the production of herbal drugs, different plant parts of the same or related species can serve as adulterants for primary plant material. The use of highly informative and relatively easily accessible tools, such as liquid chromatography and low-resolution mass spectrometry, helps to solve these tasks by means of fingerprint analysis. In this study, to reveal specific plant part features for 20 species from one family (Apiaceae), and to preserve the maximum information content, two approaches are suggested. In both cases, minimal raw data pretreatment, including rescaling of time and m/z axes and cutting off some uninformative regions, was applied. For the support vector machine (SVM) method, tensor unfolding was required, while neural networks (NNs) were able to work directly with squared heatmaps as input data. Moreover, five data augmentation variants are proposed, to overcome the typical problem of a lack of data. As a result, a comparable F1-score close to 0.75 was achieved by SVM and two employed NN architectures. Eight marker compounds belonging to chlorophylls, lipids, and coumarin apio-glucosides were tentatively identified as characteristic of their corresponding sample groups: roots, stems, leaves, and fruits. The proposed approaches are simple, information-saving and can be applied to a broad type of tasks in metabolomics.

An application of the standardised reference extract quantification strategy in the quality control of ginseng infusions by liquid chromatography with mass spectrometric detection.

INTRODUCTION: Limited availability of individual standards is a bottleneck for quality control of functional foods and natural medicines. The use of standard mixtures or secondary standards is a possible alternative in this case. Earlier, an approach known as standardised reference extract (RE) strategy was introduced for HPLC-UV analysis of different plant materials; however, its application in HPLC-MS analysis has not been investigated. OBJECTIVE: To establish an HPLC-MS-based RE method for determination of ginsenoside content in ginseng infusions using commercially available extract reference material of Panax quinquefolius L. RESULTS: The developed HPLC-MS method was validated as precise (1.1%-9.4% intra-day variation; 1.6%-12.8% inter-day variation) and highly sensitive [limit of detection (LOD): 1-40 ng/mL; limit of quantification (LOQ): 4-120 ng/mL]. The stability of samples was satisfactory (5.7%-16.3%). The RE quantification method was compared with the external standard method, and the obtained difference was not significant, mostly in the range of 5%-10%. Matrix effects for the diluted samples of RE and ginseng infusions, determined via the standard addition method, were in the range of 85%-115% and 80%-126%, respectively, and were also positively correlated with the ginsenoside concentration. Eleven batches of ginseng infusions from different manufacturers were analysed using the established method. CONCLUSION: The method for HPLC-MS-based ginsenoside quantification using RE as a secondary standard was established for the first time. The results of this study demonstrate that the application of the standardised RE strategy in HPLC-MS can minimise the matrix effect-related error in addition to the cost-effective quality control of herbal products, foods, and traditional medicines.

Deep learning for retention time prediction in reversed-phase liquid chromatography.

Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of unknown molecules in untargeted profiling using HPLC coupled with high-resolution mass spectrometry. Lots of approaches were developed for retention time prediction in liquid chromatography for a different number of molecules considering various molecular properties and machine learning algorithms. The recently built large retention time data set of standard compounds from the Metabolite and Chemical Entity Database (METLIN) allows researchers to create a model that can be used for retention time prediction of small molecules with wide varieties of structures and physicochemical properties. The ability to predict retention times using the largest data set was studied for different architectures of deep learning models that were trained on molecular fingerprints, and SMILES (string representation of a molecule) represented as one-hot matrices. The best result was achieved with a one-dimensional convolutional neural network (1D CNN) that uses SMILES as an input. The proposed model reached the mean absolute error and the median absolute error equal to 34.7 and 18.7 s, respectively, which outperformed the results previously obtained for this data set. The pre-trained 1D CNN on the METLIN SMRT data set was transferred on five other data sets to evaluate the generalization ability.

Unsupervised methods in LC-MS data treatment: Application for potential chemotaxonomic markers search.

The combination of Liquid Chromatography and Mass Spectrometry (LC-MS) is commonly used to determine and characterize biologically active compounds because of its high resolution and sensitivity. In this work we explore the interpretation of LC-MS data using multivariate statistical analysis algorithms to extract useful chemical information and identify clusters of similar samples. Samples of leaves from 19 plants belonging to the Apiaceae family were analyzed in unified LC conditions by high- and low-resolution mass spectrometry in a wide range scan mode. LC-MS data preprocessing was performed followed by statistical analysis using tensor decomposition in the form of Parallel Factor Analysis (PARAFAC); matrix factorization following tensor unfolding with principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF); or unsupervised feature selection (UFS). The optimal number of components for each of these methods were found and results were compared using four different metrics: silhouette score, Davies-Bouldin index, computational time, number of noisy components. It was found that PCA, ICA and UFS give the best results across the majority of the criteria for both low- and high-resolution data. An algorithm for biomarker signal selection is suggested and 23 potential chemotaxonomic markers were tentatively identified using MS2 data. Dendrograms constructed by the methods were compared to the molecular phylogenic tree by calculating pixel-wise mean square error (MSE). Therefore, the suggested approach can support chemotaxonomic studies and yield valuable chemical information for biomarker discovery.

A novel simple and sensitive approach for determination of 1,1-dimethylhydrazine in aqueous samples by high performance liquid chromatography with ultraviolet and tandem mass spectrometric detection after derivatization with unsubstituted aromatic aldehydes.

In this work, simple, rapid and highly sensitive method of hazardous chemical 1,1-dimethylhydrazine (unsymmetrical dimethylhydrazine, UDMH) determination based on pre-column derivatization with unsubstituted aromatic aldehydes and reversed-phase high performance liquid chromatography-ultraviolet-tandem mass spectrometry (RP HPLC-UV-MS/MS) has been developed. Along with benzaldehyde, commercially available aromatic aldehydes, namely: 2-naphthaldehyde, 2-pyridinecarboxaldehyde, and 2-quinolinecarboxaldehyde, were used as derivatizing reagents in the analysis of hydrazines for the first time. The reactions were studied in a wide pH range by varying reaction time and other conditions. A slightly alkaline pH 9 was shown to be optimal for the derivatization of UDMH by aromatic aldehydes. The quantitative yield of derivatization products under the established conditions was confirmed by HPLC analysis with amperometric detection. For all studied reagents, wide linear ranges of concentrations (0.01-1000 µg/L) in natural water samples were observed. The limits of detection for UDMH in natural water were in the 3.7-130 ng/L range. 2-Quinolinecarboxaldehyde was selected as the most appropriate reagent for HPLC-UV-MS/MS determination of UDMH. In case of using this reagent, the accuracy was in the range of 97-102%, and precision, expressed as RSD was less than 8%. The developed approach does not require laborious stages of pre-concentration and isolation of UDMH from natural water components.

Quantitative aspects of the hydrolysis of ginseng saponins: Application in HPLC-MS analysis of herbal products.

BACKGROUND: Ginseng is one of the most valuable herbal supplements. It is challenging to perform quality control of ginseng products due to the diversity of bioactive saponins in their composition. Acid or alkaline hydrolysis is often used for the structural elucidation of these saponins and sugars in their side chains. Complete transformation of the original ginsenosides into their aglycones during the hydrolysis is one of the ways to determine a total saponin group content. The main hurdle of this approach is the formation of various by-products that was reported by many authors. METHODS: Separate HPLC assessment of the total protopanaxadiol, protopanaxatriol and ocotillol ginsenoside contents is a viable alternative to the determination of characteristic biomarkers of these saponin groups, such as ginsenoside Rf and pseudoginsenoside F11, which are commonly used for authentication of P. ginseng Meyer and P. quinquefolius L. samples respectively. Moreover, total ginsenoside content is an ideal aggregated parameter for standardization and quality control of ginseng-based medicines, because it can be directly applied for saponin dosage calculation. RESULTS: Different hydrolysis conditions were tested to develop accurate quantification method for the elucidation of total ginsenoside contents in herbal products. Linearity, limits of quantification, limits of detection, accuracy and precision were evaluated for the developed HPLC-MS method. CONCLUSION: Alkaline hydrolysis results in fewer by-products than sugar elimination in acidic conditions. An equimolar response, as a key parameter for quantification, was established for several major ginsenosides. The developed approach has shown acceptable results in the analysis of several different herbal products.

A new PARAFAC-based algorithm for HPLC-MS data treatment: herbal extracts identification.

INTRODUCTION: Role of highly informative high-performance liquid chromatography mass spectrometry (HPLC-MS) methods in quality control is increasing. Complex herbal products and formulations can simultaneously contain extracts from different plants. Therefore, due to the leads to lack of commercial standards it is important to develop novel approaches for comprehensive treatment of big datasets. OBJECTIVE: The aim of this study is to create a straightforward and information-saving algorithm for the identification of plants extracts in commercial products. MATERIAL AND METHODS: In total, 34 samples, including Glycyrrhiza glabra and Panax ginseng dried roots; and Abrus precatorius dried leaves, their double and triple mixtures and flavoured oolong tea samples were analysed by HPLC-MS and combined in a three-dimensional dataset (retention time-mass-to-charge ratio (m/z)-samples). This dataset was subjected to smoothing and denoising techniques and further decomposed using parallel factor analysis (PARAFAC). RESULTS: Samples were divided into eight clusters; loading matrices were interpreted and the presence of the most characteristic triterpene glycoside groups was demonstrated and supported by the characteristic chromatogram approach. The occurrence of Abrus precatorius and G. glabra additives in flavoured tea was confirmed. CONCLUSION: Developed HPLC-MS-PARAFAC method is potentially reliable and an efficient tool for handling untreated experimental data and its future development may lead to more comprehensive evaluation of chemical composition and quality control of food additives and other complex mixtures.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines.

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

The comparison of retention behaviour of imidazoline and serotonin receptor ligands in non-aqueous hydrophilic interaction chromatography and supercritical fluid chromatography.

This work presents an investigation of retention characteristics of imidazoline and serotonin receptor ligands in non-aqueous hydrophilic interaction liquid chromatography (NA-HILIC) and supercritical fluid chromatography (SFC). The separation has been carried out by using methanol as a mobile phase modifier with addition of two types of additives (NH4HCOO; NH4HCOO/HCOOH) and two different stationary phases (diol; mixed-mode diol). The selectivity characteristics were observed based on S-factors, logk-logk plots and radar plots. NA-HILIC vs. SFC retention of tested compounds was also described by considering the molecular properties of the analytes within the LSER analysis. The differences between SFC vs. NA-HILIC retention of imidazoline and serotonin receptor ligands grow with the acid addition to a mobile phase, noticeably on mixed-mode diol stationary phase (S ≥ 87). In addition, the good selectivity performances of the certain NA-HILIC and SFC conditions were confirmed by good separation of structurally related compounds (α ≥ 2). The molecular basis of NA-HILIC and SFC retention were explained by using Abraham's equation. The dominant analyte descriptors influencing retention were hydrogen bonding and dipolar interactions. The current study will present the theory, and discuss the applicability within the SFC vs. NA-HILIC regimes. In this way, it was provided the placing of two relatively new methods (SFC, NA-HILIC) in the map of modern analytical chromatography in terms of the pharmaceutical analysis.

Nerve agent markers screening after accumulation in garden cress (Lepidium sativum) used as a model plant object.

In this research an accumulation of nerve agent markers in garden cress (Lepidium sativum) as a model plant object was studied using LC-QTOF hybrid system. For the determination of methylphosphonic acid and alkyl methylphosphonates, which are specific markers of sarin, soman, VR and VX, simple and sensitive approach was developed. Direct analysis of aqueous extracts on the reversed phase column with polar endcapping allowed to achieve satisfactory retention factor for methylphosphonic acid, which has high polarity and is usually very weakly retained on the ordinary reversed phase columns. Application of the QTOF mass spectrometer with high mass resolution led to the increase in the accuracy of the conducted measurements. The HPLC-HRMS technique developed exclusively for this study has been validated for linearity, limit of detection, limit of quantification, precision, accuracy and matrix effect prior to the analysis of plant extract samples. Hydroponic growth model was employed to examine accumulation of nerve agent markers in garden cress. It was found that after elimination of nerve agent markers from the plant growth medium, garden cress was able to store these substances for at least 5 weeks providing high retrospectivity of the analysis. Moreover, during the cress growth, no metabolization of alkyl methylphosphonates was observed. This allows not only to reveal the fact of nerve agents release into environment, but also to define its type after a long period of time.

Impact of weak water deficit on growth, photosynthetic primary processes and storage processes in pine and spruce seedlings.

We investigated the influence of 40 days of drought on growth, storage processes and primary photosynthetic processes in 3-month-old Scots pine and Norway spruce seedlings growing in perlite culture. Water stress significantly affected seedling water status, whereas absolute dry biomass growth was not substantially influenced. Water stress induced an increase in non-structural carbohydrate content (sugars, sugar alcohols, starch) in the aboveground part of pine seedlings in contrast to spruce seedlings. Due to the relatively low content of sugars and sugar alcohols in seedling organs, their expected contribution to osmotic potential changes was quite low. In contrast to biomass accumulation and storage, photosynthetic primary processes were substantially influenced by water shortage. In spruce seedlings, PSII was more sensitive to water stress than PSI. In particular, electron transport in PSI was stable under water stress despite the substantial decrease of electron transport in PSII. The increase in thermal energy dissipation due to enhancement of non-photochemical quenching (NPQ) was evident in both species under water stress. Simultaneously, the yields of non-regulated energy dissipation in PSII were decreased in pine seedlings under drought. A relationship between growth, photosynthetic activities and storage processes is analysed under weak water deficit.

Application of quantitative analysis of multi-component system approach for determination of ginsenosides in different mass-spectrometric conditions.

The lack of standard substances is a bottleneck for quality control in traditional and alternative medicine. Development of the HPLC-UV approaches combined with single standard for quantitative analysis of multi-component system (QAMS) allowed determination of several plant biomarkers by implementation of the relative response factors (RRFs). Robustness and ruggedness of such methods are commonly demonstrated by performing the analysis in changing analytical conditions on the different HPLC equipment and columns. The nature of MS detection is much more complicated and dependent on the instrumentation. Therefore, this study was conducted to justify the use of RRFs for HPLC-MS determination of bioactive compounds from plants. Protopanaxatriol (PPT), protopanaxadiol (PPD) and ocotillol (OT) ginseng saponins (ginsenosides) were successfully separated on a reversed-phase PFP-column with high group selectivity. Fragmentation patterns for these groups of compounds were established on different HPLC-ESI-MS systems and at varied declustering potentials (DPs). The use of sapogenin fragmentation ions in positive detection mode along with group reference standards was shown to be an optimal way to perform quantification. The performance of the developed group targeted HPLC-MS-QAMS approach was tested in the course of measurements conducted on the different instrumentation. The differences between QAMS and external standard method (ESM) quantification results were below 15% for all determined saponins.

Unified strategy for HPLC-MS evaluation of bioactive compounds for quality control of herbal products.

Evaluation of safety, quality and composition of herbal products and food supplements based on botanical ingredients is a matter of serious concern. For screening of botanicals in herbal products multitargeted and group-targeted approaches may be applied. In the group-targeted approach botanicals are characterized by means of an appropriate group of structurally related biomarkers compared with the multitargeted approach where a number of selected analytes are monitored based on a multiple reaction monitoring survey. In this study a unified strategy for quality control of herbal products was developed on the basis of fast ultrasound-assisted extraction, chromatographic separation and mass spectrometric quantification of bioactive compounds. A large list of unique biomarkers were monitored under almost identical chromatographic conditions, while an efficient strategy for HPLC-MS group-targeted analysis was also developed for comprehensive evaluation of chemical composition of botanicals intensively used for herbal product manufacturing. In the latter case, structurally close compounds were determined in a single ion monitoring mode for the characteristic group of fragment ions, allowing fast profiling and quality assessment of the plant material or complex food supplement. The sensitivity of the developed approaches was on the level of 1-50 ng/mL, which is higher than that of existing HPLC-UV quality control methods.

LC-MS determination of steroidal glycosides from Dioscorea deltoidea Wall cell suspension culture: Optimization of pre-LC-MS procedure parameters by Latin Square design.

In this paper, the ultrasound assisted extraction method for isolation of steroidal glycosides from D. deltoidea plant cell suspension culture with a subsequent HPLC-MS determination was developed. After the organic solvent was selected via a two-factor experiment the optimization via Latin Square 4 × 4 experimental design was carried out for the following parameters: extraction time, organic solvent concentration in extraction solution and the ratio of solvent to sample. It was also shown that the ultrasound assisted extraction method is not suitable for isolation of steroidal glycosides from the D. deltoidea plant material. The results were double-checked using the multiple successive extraction method and refluxing extraction. Optimal conditions for the extraction of steroidal glycosides by the ultrasound assisted extraction method were: extraction time, 60 min; acetonitrile (water) concentration in extraction solution, 50%; the ratio of solvent to sample, 400 mL/g. Also, the developed method was tested on D. deltoidea cell suspension cultures of different terms and conditions of cultivation. The completeness of the extraction was confirmed using the multiple successive extraction method.

Binding and Action of Amino Acid Analogs of Chloramphenicol upon the Bacterial Ribosome.

Antibiotic chloramphenicol (CHL) binds with a moderate affinity at the peptidyl transferase center of the bacterial ribosome and inhibits peptide bond formation. As an approach for modifying and potentially improving properties of this inhibitor, we explored ribosome binding and inhibitory activity of a number of amino acid analogs of CHL. The L-histidyl analog binds to the ribosome with the affinity exceeding that of CHL by 10 fold. Several of the newly synthesized analogs were able to inhibit protein synthesis and exhibited the mode of action that was distinct from the action of CHL. However, the inhibitory properties of the semi-synthetic CHL analogs did not correlate with their affinity and in general, the amino acid analogs of CHL were less active inhibitors of translation in comparison with the original antibiotic. The X-ray crystal structures of the Thermus thermophilus 70S ribosome in complex with three semi-synthetic analogs showed that CHL derivatives bind at the peptidyl transferase center, where the aminoacyl moiety of the tested compounds established idiosyncratic interactions with rRNA. Although still fairly inefficient inhibitors of translation, the synthesized compounds represent promising chemical scaffolds that target the peptidyl transferase center of the ribosome and potentially are suitable for further exploration.

Structure elucidation of sweet-tasting cycloartane-type saponins from ginseng oolong tea and Abrus precatorius L. leaves.

The composition of ginseng oolong a widely used tea product was investigated. A new sweet-tasting cycloartane-type saponin 3ß-O-[ß-D-glucuronopyranosyl(1→2)]-ß-D-6-acetylglucopyranosyl)-(20S,22S)-3ß,22-dihydroxy-9,19-cyclolanost-24-en-26,29-oic acid (9), along with five new compounds, was identified in the methanolic extract of the ginseng oolong tea. Structural elucidation was conducted by spectroscopic methods, including 1D- and 2D-NMR, HR-MS, HPLC-LITMS/MS. It was shown that these compounds have different sugar chains connected to the abrusogenin molecule. Then saponin profiles of four commercially available ginseng oolong samples and Abrus precatorius L. leaves were compared and the presence of three known abrusosides and six new acetyl and malonyl abrusogenin derivatives was proved. The original ginsenosides were not detected in the extract, therefore, abrusosides could serve as a replacement responsible for the sweet taste of the tea.

Rapid IC-MS/MS determination of methylphosphonic acid in urine of rats exposed to organophosphorus nerve agents.

A direct approach for the determination of a specific hydrolysis product of organophosphorus nerve agents such as methylphosphonic acid (MPA) in urine by ion chromatography and tandem mass spectrometry (IC-MS/MS) has been developed. The first advantage of the proposed approach is a rapid and simple sample preparation, which does not require a large sample volume, complicated and laborious preconcentration and derivatization steps, and takes less than 7min per sample. The second advantage is the fast and selective IC determination of MPA carried out on a noncommercial anion exchanger based on a poly(styrene-co-divinylbenzene) (PS-DVB) substrate with a high degree of crosslinking and a covalently-bonded branched functional layer, which enables complete resolution of MPA from major urine matrix components and allows one to overcome matrix effects. Hyphenation of IC with tandem mass spectrometry results in highly sensitive and reliable MPA determination with the lowest detection limit (4ngmL-1) reported so far for HPLC determination of MPA in urine. The proposed approach is successfully applied for the analysis of urine from rats exposed to nonlethal doses of organophosphorus nerve agents such as sarin, soman, and VR in up to 13days after initial exposure, which shows the possibility to verify the nerve agent exposure after a long period of time.